Symbolic Analysis of High-Dimensional Time Series

2003 ◽  
Vol 13 (09) ◽  
pp. 2657-2668 ◽  
Author(s):  
Karsten Keller ◽  
Heinz Lauffer
Keyword(s):  
Time Series ◽  
Symbolic Dynamics ◽  
Epileptic Activity ◽  
High Dimensional ◽  
Qualitative Information ◽  
Eeg Data ◽  
Ordinal Patterns ◽  
The Given ◽  
Matrix Series ◽  
Qualitative Changes

In order to extract and to visualize qualitative information from a high-dimensional time series, we apply ideas from symbolic dynamics. Counting certain ordinal patterns in the given series, we obtain a series of matrices whose entries are symbol frequencies. This matrix series is explored by simple methods from nominal statistics and information theory. The method is applied to detect and visualize qualitative changes of EEG data related to epileptic activity.

DISTANCES OF TIME SERIES COMPONENTS BY MEANS OF SYMBOLIC DYNAMICS

2004 ◽  
Vol 14 (02) ◽  
pp. 693-703 ◽  
Author(s):  
KARSTEN KELLER ◽  
KATHARINA WITTFELD
Keyword(s):  
Time Series ◽  
Symbolic Dynamics ◽  
Time Dependent ◽  
High Dimensional ◽  
Simple Method ◽  
Original Series ◽  
One Dimensional ◽  
Pattern Type ◽  
Eeg Data

In this note we describe a simple method for visualizing time-dependent similarities and dissimilarities between the components of a high-dimensional time series. On the base of symbolic dynamics, the time series is turned into a series of matrices whose rows quantify pattern types in the components of the original series. For different scales we introduce distances between the components via the obtained pattern type distributions and approximate them in a one-dimensional manner. The method is illustrated for 19-channel EEG data.

scholarly journals Detection of time reversibility in time series by ordinal patterns analysis

2018 ◽  
Vol 28 (12) ◽  
pp. 123111 ◽  
Author(s):  
J. H. Martínez ◽  
J. L. Herrera-Diestra ◽  
M. Chavez
Keyword(s):  
Time Series ◽  
Time Reversibility ◽  
Ordinal Patterns

Weak Signal Detection in Underwater Acoustic Based on Time-Series Characteristic

2011 ◽  
Vol 216 ◽  
pp. 548-552
Author(s):  
Hai Ping Wu ◽  
Shi Jian Zhu ◽  
Jing Jun Lou ◽  
Li Yang Yu
Keyword(s):  
Time Series ◽  
Signal Detection ◽  
Matched Filter ◽  
Doppler Frequency ◽  
High Dimensional ◽  
Filter Method ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Underwater Acoustic ◽  
Radar Echo

For limitation of the matched filter method in underwater acoustic detection,a method of underwater acoustic weak signal detection based on time series characteristic quantity is proposed．Chaotic waveforms, which have thumbtack type ambiguity function, is selected as the waveform of active sonar in the situation of High Dynamic Doppler Frequency Shift. According to the change of correlation dimension while chaotic radar echo appears in the chaotic background, chaotic radar echo is checked out by the means of simulation in the situation of high dimensional chaotic background and low dimensional chaotic background．The method proves out in high dimensional chaotic background．

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

scholarly journals LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4112 ◽  
Author(s):  
Se-Min Lim ◽  
Hyeong-Cheol Oh ◽  
Jaein Kim ◽  
Juwon Lee ◽  
Jooyoung Park
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
State Space Model ◽  
Skill Assessment ◽  
Series Data ◽  
High Dimensional ◽  
Table Tennis ◽  
Space Model ◽  
Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

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